Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Symmetry-Breaking Convergence Analysis of Certain Two-layered Neural Networks with ReLU nonlinearity
Authors: Yuandong Tian
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations verify our theoretical analysis. ... Sec. 5 shows that simulation results are consistent with theoretical analysis. |
| Researcher Affiliation | Industry | Yuandong Tian Facebook AI Research EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link for open-sourcing its code. |
| Open Datasets | No | The paper assumes that the input x follows Gaussian distribution (synthetic data assumption) but does not mention the use of any publicly available or open real-world dataset with access information. It states: "We assume that the input x follow Gaussian distribution." and "We prepare the input data X with standard Gaussian distribution". |
| Dataset Splits | No | The paper does not mention specific training, validation, or test dataset splits for any real-world data. It analyzes theoretical dynamics with assumed Gaussian input distribution. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its simulations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for its simulations or analysis. |
| Experiment Setup | No | The paper describes the theoretical setup and assumptions (e.g., Re LU nonlinearity, Gaussian input, teacher-student setting) but does not provide specific experimental setup details such as hyperparameters (learning rate, batch size, epochs, optimizers) for training a neural network model. It focuses on the dynamics analysis. |